Data & AI Engineering

A NetSuite data warehouse on Databricks succeeds or fails on the gold model and the trial balance tie-out, not the connector. Here is how to build one that finance will sign off.

Three ways to get SAP data into the Databricks lakehouse, compared on copies, semantics, latency and lock-in, with the process red flags to clear before you connect anything.

Most Splunk alternative lists compare log search tools. Security teams under ingest-cost pressure are really choosing where their data lives. Here are seven real options and how to judge them.

dbt and Lakeflow Declarative Pipelines both transform data on a Databricks lakehouse. Here is how to decide which one owns which layer, and how to run dbt well if you keep it.

A practical data quality framework for the Databricks lakehouse: where Lakeflow expectations, DQX and Great Expectations fit, and how DMAIC turns rules into a control system.

Lakeflow Connect gives the Databricks lakehouse managed, serverless ingestion for common SaaS apps and databases. Here is how it works, where it stops, and how to decide between a managed connector and your own build.

A mid-market playbook for Teradata to Databricks migration: sizing the estate, converting BTEQ and stored procedures, moving data with TPT, reconciling results and knowing when you are ready to cut over.

Azure Data Factory and Databricks overlap more than they used to. This comparison shows what each does best, how each bills, and when a mid-market Azure team should consolidate orchestration on Lakeflow.

A security data lake is a data engineering build, not a product purchase. Here is how to land raw logs, normalise them to OCSF and govern them on the Databricks lakehouse.